.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "generated\examples\tutorials\plot_coreg_options.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_generated_examples_tutorials_plot_coreg_options.py: =============================================== Coregistration options in mdreg =============================================== By default, ``mdreg`` performs coregistration using the optical flow method :func:`~skimage.optical_flow_tvl1` as implemented in skimage, with default settings for all parameters. The coregistration can be modified either by overriding these defaults, or by coregistering with one of the optional coregistration engines wrapped in mdreg: *ants* and *elastix*. This example shows how these options can be customized to achieve the best results. .. GENERATED FROM PYTHON SOURCE LINES 19-21 Setup ----- .. GENERATED FROM PYTHON SOURCE LINES 21-24 .. code-block:: Python import numpy as np import mdreg .. GENERATED FROM PYTHON SOURCE LINES 25-26 Load test data .. GENERATED FROM PYTHON SOURCE LINES 26-39 .. code-block:: Python data = mdreg.fetch('MOLLI') array = data['array'][:,:,0,:] # Throughout this example we use the same signal model: molli = { 'func': mdreg.fit_abs_exp_recovery_2p, 'TI': np.array(data['TI'])/1000, } # Visualise the data anim = mdreg.plot.animation(array, vmin=0, vmax=1e4) .. container:: sphx-glr-animation .. raw:: html

.. GENERATED FROM PYTHON SOURCE LINES 40-45 Coregistration with ``skimage`` ------------------------------- If we don't provide any detail on the coregistration method, ``mdreg`` uses the optical flow method :func:`~skimage.optical_flow_tvl1` from the package ``skimage`` for coregistration: .. GENERATED FROM PYTHON SOURCE LINES 45-51 .. code-block:: Python coreg, fit, _, _ = mdreg.fit( array, fit_image=molli, ) .. GENERATED FROM PYTHON SOURCE LINES 52-53 Visualise the results .. GENERATED FROM PYTHON SOURCE LINES 53-55 .. code-block:: Python anim = mdreg.plot.series(array, fit, coreg, vmin=0, vmax=1e4) .. container:: sphx-glr-animation .. raw:: html

.. GENERATED FROM PYTHON SOURCE LINES 56-67 This has clearly had some effect, but the motion correction is not perfect and the coregistered image is somewhat blurred. We could try to improve the result by optimizing the parameters. One parameter in :func:`~skimage.optical_flow_tvl1` is the *attachment*, which by default is set to 15, but can be set to smaller values for smoother results. In this case we want the opposite, so let's see what happens when we set it to 30. We can do this by providing the *fit_coreg* argument to *mdreg.fit*: .. GENERATED FROM PYTHON SOURCE LINES 67-77 .. code-block:: Python coreg, fit, _, _ = mdreg.fit( array, fit_image=molli, fit_coreg={'attachment': 30}, ) # Visualise the results anim = mdreg.plot.series(array, fit, coreg, vmin=0, vmax=1e4) .. container:: sphx-glr-animation .. raw:: html

.. GENERATED FROM PYTHON SOURCE LINES 78-80 This appears to have had the desired effect and has resulted in a better motion correction. .. GENERATED FROM PYTHON SOURCE LINES 83-94 Coregistration with elastix --------------------------- An other approach to optimization is to use another coregistration engine. ``mdreg`` also wraps elastix, so let's try that. By default `mdreg` applies ``elastix`` with bspline deformations, and default settings for all parameters. The result can be further customized by providing any valid ``elastix`` parameter in the dictionary. For this example we will change the default grid spacing from 16mm to 50mm. Since elastix is not default, we need to provide the package name as well. It also needs the pixel spacing so that dimensions in mm are well defined: .. GENERATED FROM PYTHON SOURCE LINES 94-109 .. code-block:: Python coreg, fit, _, _ = mdreg.fit( array, fit_image=molli, fit_coreg={ 'package': 'elastix', 'spacing': data['pixel_spacing'], 'FinalGridSpacingInPhysicalUnits': 50.0, }, ) # Visualise the results anim = mdreg.plot.series(array, fit, coreg, vmin=0, vmax=1e4) .. container:: sphx-glr-animation .. raw:: html

.. GENERATED FROM PYTHON SOURCE LINES 110-119 Coregistration with ants --------------------------- Apart from ``skimage`` and ``elastix``, mdreg also wraps ``ants`` as a registration engine. It can be called like elastix, by specifying the package and, if needed, any of the parameters to be set to different values than the default. We illustrate this here by coregistering with a transform type different from the default: .. GENERATED FROM PYTHON SOURCE LINES 119-129 .. code-block:: Python coreg, fit, _, _ = mdreg.fit( array, fit_image=molli, fit_coreg={ 'package': 'ants', 'type_of_transform': 'SyNOnly', }, ) .. rst-class:: sphx-glr-script-out .. code-block:: none invalid value encountered in cast .. GENERATED FROM PYTHON SOURCE LINES 130-131 Looking at the results, this is not a great solution for these data. .. GENERATED FROM PYTHON SOURCE LINES 131-134 .. code-block:: Python anim = mdreg.plot.series(array, fit, coreg, vmin=0, vmax=1e4) .. container:: sphx-glr-animation .. raw:: html

.. rst-class:: sphx-glr-timing **Total running time of the script:** (55 minutes 9.884 seconds) .. _sphx_glr_download_generated_examples_tutorials_plot_coreg_options.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_coreg_options.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_coreg_options.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_coreg_options.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_